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Using Deep Neural Networks with Intra- and Inter-Sentence Context to Classify Suicidal Behaviour

Research output: Contribution to journalArticle

Xingyi Song, Johnny Downs, Sumithra Velupillai, Rachel Holden, Maxim Kikoler, Kalina Bontcheva, Rina Dutta, Angus Roberts

Original languageEnglish
Pages (from-to)1296–1303
Number of pages8
JournalProceedings of the 12th International Conference on Language Resources and Evaluation (LREC 2020)
Volume1296–1303
Early online date15 May 2020
Publication statusPublished - 15 May 2020

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Abstract

Identifying statements related to suicidal behaviour in psychiatric electronic health records (EHRs) is an important step when modeling that behaviour, and when assessing suicide risk. We apply a deep neural network based classification model with a lightweight context encoder, to classify sentence level suicidal behaviour in EHRs. We show that incorporating information from sentences to left and right of the target sentence significantly improves classification accuracy. Our approach achieved the best performance when classifying suicidal behaviour in Autism Spectrum Disorder patient records. The results could have implications for suicidality research and clinical surveillance.

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